Are there any examples of "Hello World!" with simulated basic brain cells? I'm sure this is an entirely naive question given the complexity of a brain, but I'm imagining a rudimentary program to help understand brain-style processing with some kind of brain cell struct unit that represents real-ish input/output mechanisms that can be connected to other brain cell struct units...leading to some minimal brain-style processing outcomes.
About simulation: we do have simulators of brains (to some extent). The main example is the simulation of the C. Elegans. One such simulator is on GitHub at https://github.com/Flowx08/Celegans-simulation
The problem is, well before tackling the human brain - with functional units, wiring, evolutionary rewiring¹, modules, multiform glias and mysteries in general etc. - that of transparency, i.e. already in the C. Elegans you can see complex behaviour in the studied system, but the hard part, that of understanding why and how that structure presents that behaviour, is unachieved. It is still a "black box" like some ANNs. We know the structure of the C. Elegans since 1985, but we still do not know well "why it works, why it works that way": the "scientific problem" of understanding the "natural phenomenon" we found, in order to export that understanding, is still pending.
So: we have simple brains described, they are simulated, and to just look at them will not make you much wiser until you really manage to "crack the code". There is not really an «Hello World» yet. It does not mean your doctoral efforts could not achieve it though!
--
¹ Evolutionary rewiring: the human nervous system is also made by "afterthought" increases which overlapped on pre-existing structures "patching" them, instead of "correcting, reprogramming" them - so one will find a system that may show to be a masterpiece of cleverness but not a masterpiece of planning. In other words, the human brain is something relatively messy, not a clean blueprint.
This feels easily related to lack of unified physical theories.
We seem to find structure all over but no idea why it ended up just this way.
My guess is a link between field effects and information coming off cells. Cells emit information instigating a field reaction but only to a certain point as a survival mechanism.
Once an equilibrium is reached the cells radiate new information to inhibit the field effect?
Which makes me wonder if cells not being able to inhibit field effects is related to aging.
I picked the wrong field in college. Math was fun but the kind of abstract that almost feels useless a lot of the time, and biotech really feels like the final frontier anymore.
I think you vastly overestimate our current understanding of the brain/neuronal processing, even singular (neuron) cell's workings. The fMRI enthusiasm turned out wrong a long time ago and there hasn't been much progress since, for all I know. And knowing a gene sequence wasn't enough either. We are still lacking theory big time. For most all of biology, we do not understand anything fully really. Not in neuro science, not in evolution theory, and god forbid not in genetics. Complex != complicated. Complexity is a hard barrier. We do not have abstraction for emergence, merely still recording thing's reactions when you poke them in different ways.
Do we even understand how DNA replication works? It's a relatively simple mechanical process, but it's performed by molecular mechanisms that exhibit oddly intelligent behavior.
The Human Brain Project is a somewhat failed experiment to understand the brain in its entirety. They started by taking human brain cross sections and imaging them down to neuronal resolution, but the scans took up so much space that there wouldn't be enough capacity on the entire planet to store a complete human brain scan. This is a challenge of compression inasmuch as it is a challenge of understanding brains.
I question if progress in any given field X is completely and unequivocally hindered by progress in a dependent field Y. If this is the case, dependencies better be known up front before embarking on any project, unless you like wasting time and money.
Ray Kurzweil presents one theory about the basic neuronal modules in How to Create a Mind (2012). The text is of course controversial - criticism may be as important as the tentative idea presented.
If you want to understand how the brain works this is a good intro with some realistic neuronal network models ( spoiler: these have nothing to do with “artificial neural nets” as we know them) https://www.amazon.com/Brain-Computations-Edmund-T-Rolls/dp/...
Neuroscience is fascinating, well at least the part about image processing, but it's also completely inaccessible. From computer science and programming we are use to having introduction available in the form of articles, videos and cheap training. For Neuroscience it's just straight up hard-mode and you don't know any of the words. I still not even completely sure what a brain atlas is.
I've seen a few free university courses, but they fill up almost immediately.
YT, for example, contains many resources on brain anatomy and more. Plus, for example, the lessons from Robert Sapolsky at Stanford.
Plus, there exist the primer books from Rita Carter (first of all, Mapping the Mind).
There are loads of good material around. Note though, that CS is "forward" engineering, while Brain Science is backward engineering. So, if you do not find material that build from the ground up... it is easily because we are not at that level.
So I expected this to be about the human brain since sites like http://mousebrain.org/ already existed, but it looks like it's research that has been done on mice brains as well? Can someone explain the differences here?
Hey vanderZwan, we have a paper in the collection (there’s 17 brain atlas papers in this issue of Nature) on human, marmoset and mouse motor cortex (my group did the human). I think it’s cool that pretty much all the cell types could be aligned between those species, which diverged 90 million years ago.
Our full human brain cell type atlas is work in progress but hopefully we’ll post the preprint early next year.
the headline is attention seeking since there are numerous pre-existing brain atlases that describe the anatomy/cellular/molecular parts in different ways. You can check https://portal.brain-map.org/ for many examples.
I think a good comparison to this kind of work is what has happened previously in astronomy, where scientists work to answer their specific questions using large open datasets acquired using shared infrastructure (fancy telescopes for astronomy, microscopes for biology).
